2,023 research outputs found
PC based offline Arabic text recognition system
Character recognition systems can contribute tremendously to the advancement of automation process and can improve the interaction between man and machine in many applications. In this paper we describe a PC based system for offline recognition of Arabic characters and numerals. The system is based on expressing the machine printed Arabic alpha-numeric text in terms of strokes obtained by modified MCR Expression [Chinveerapphan, S. et al., Apr. 1995]. The system is implemented on a PIII machine in visual programming language under Windows environment
PC based offline Arabic text recognition system
Character recognition systems can contribute tremendously to the advancement of automation process and can improve the interaction between man and machine in many applications. In this paper we describe a PC based system for offline recognition of Arabic characters and numerals. The system is based on expressing the machine printed Arabic alpha-numeric text in terms of strokes obtained by modified MCR Expression [Chinveerapphan, S. et al., Apr. 1995]. The system is implemented on a PIII machine in visual programming language under Windows environment
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform
In this research, off-line handwriting recognition system for Arabic alphabet is
introduced. The system contains three main stages: preprocessing, segmentation and
recognition stage. In the preprocessing stage, Radon transform was used in the design
of algorithms for page, line and word skew correction as well as for word slant
correction. In the segmentation stage, Hough transform approach was used for line
extraction. For line to words and word to characters segmentation, a statistical method
using mathematic representation of the lines and words binary image was used.
Unlike most of current handwriting recognition system, our system simulates the
human mechanism for image recognition, where images are encoded and saved in
memory as groups according to their similarity to each other. Characters are
decomposed into a coefficient vectors, using fast wavelet transform, then, vectors,
that represent a character in different possible shapes, are saved as groups with one
representative for each group. The recognition is achieved by comparing a vector of
the character to be recognized with group representatives.
Experiments showed that the proposed system is able to achieve the recognition task
with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a
single character in a text of 15 lines where each line has 10 words on average
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